Development of a Prognostic Model for Six-Month Mortality in Older Adults With Declining Health

ContextEstimation of six-month prognosis is essential in hospice referral decisions, but accurate, evidence-based tools to assist in this task are lacking.ObjectivesTo develop a new prognostic model, the Patient-Reported Outcome Mortality Prediction Tool (PROMPT), for six-month mortality in community-dwelling elderly patients.MethodsWe used data from the Medicare Health Outcomes Survey linked to vital status information. Respondents were 65 years old or older, with self-reported declining health over the past year (n = 21,870), identified from four Medicare Health Outcomes Survey cohorts (1998–2000, 1999–2001, 2000–2002, and 2001–2003). A logistic regression model was derived to predict six-month mortality, using sociodemographic characteristics, comorbidities, and health-related quality of life (HRQOL), ascertained by measures of activities of daily living and the Medical Outcomes Study Short Form-36 Health Survey; k-fold cross-validation was used to evaluate model performance, which was compared with existing prognostic tools.ResultsThe PROMPT incorporated 11 variables, including four HRQOL domains: general health perceptions, activities of daily living, social functioning, and energy/fatigue. The model demonstrated good discrimination (c-statistic = 0.75) and calibration. Overall diagnostic accuracy was superior to existing tools. At cut points of 10%–70%, estimated six-month mortality risk sensitivity and specificity ranged from 0.8% to 83.4% and 51.1% to 99.9%, respectively, and positive likelihood ratios at all mortality risk cut points ≥40% exceeded 5.0. Corresponding positive and negative predictive values were 23.1%–64.1% and 85.3%–94.5%. Over 50% of patients with estimated six-month mortality risk ≥30% died within 12 months.ConclusionThe PROMPT, a new prognostic model incorporating HRQOL, demonstrates promising performance and potential value for hospice referral decisions. More work is needed to evaluate the model.